Retail Media Decisions: Agencies Using Voice AI to Prioritize Channels

A performance marketing agency recently faced a familiar dilemma: their CPG client wanted to shift 40% of their media budget into retail media networks, but the internal team couldn't agree on which channels deserved priority. Walmart Connect showed strong ROAS in Q3. Amazon Advertising delivered volume but squeezed margins. Instacart and Kroger Precision Marketing remained largely untested. The client needed a recommendation within two weeks.

Traditional research couldn't meet that timeline. Focus groups required 4-6 weeks of recruiting and scheduling. Survey data would capture stated preferences but miss the contextual nuances of actual shopping behavior. The agency needed to understand how real customers discovered products, evaluated options, and made purchase decisions across different retail environments—and they needed those insights fast enough to inform budget allocation.

This scenario reflects a broader shift in retail media strategy. As brands distributed $45 billion across retail media networks in 2023 (a figure projected to reach $100 billion by 2026 according to Boston Consulting Group), the strategic question evolved from "should we invest in retail media" to "which retail media channels align with how our customers actually shop." That second question requires qualitative depth at quantitative speed—precisely the capability conversational AI delivers.

Why Channel Prioritization Demands Customer Voice

Retail media performance data reveals what happened but rarely explains why. An agency might observe that Walmart Connect generates 3.2x ROAS while Target Roundel delivers 2.1x, but those numbers don't illuminate the underlying customer behaviors driving the difference. Without understanding the "why," agencies struggle to predict performance in new categories, optimize creative approaches, or confidently scale investment.

The complexity multiplies across product categories. A customer's path to purchase for shelf-stable groceries differs fundamentally from their approach to health and beauty products or household electronics. Shopping behavior on Amazon—where customers often arrive with specific intent—contrasts sharply with discovery patterns on Instacart, where basket-building happens within narrow delivery windows. Each retail media environment creates distinct contexts for attention, consideration, and conversion.

Performance metrics capture outcomes within these contexts but can't explain the decision architecture underneath. When a customer clicks a sponsored product on Amazon versus discovering a brand through Walmart Connect's homepage placement, different psychological processes drive the interaction. Understanding these processes allows agencies to match creative strategy, messaging hierarchy, and budget allocation to actual customer behavior rather than optimizing solely against historical performance data.

Research from the Ehrenberg-Bass Institute demonstrates that availability—both physical and mental—drives brand growth more powerfully than persuasion alone. Retail media decisions essentially become availability decisions: which channels make your brand mentally and physically available at moments when customers are ready to buy? Answering that question requires understanding how customers think about categories, evaluate options, and navigate retail environments—insights that emerge through conversation, not clickstream analysis.

The Traditional Research Timeline Problem

Agencies operating in retail media face compressed decision cycles that traditional research methodologies can't accommodate. A typical scenario unfolds like this: a brand requests a retail media strategy recommendation by month-end to align with Q2 budget planning. The agency needs customer insights to inform channel prioritization, creative direction, and measurement frameworks. Traditional qualitative research requires 6-8 weeks minimum—recruiting participants, scheduling interviews, conducting sessions, analyzing transcripts, and synthesizing findings.

By the time insights arrive, the strategic moment has passed. The agency either makes decisions without customer input (relying instead on channel sales pitches and historical performance data) or delays recommendations until research completes (frustrating clients who need to move quickly in competitive categories). Neither option serves the agency's goal of delivering evidence-based strategy that differentiates their work from competitors.

The recruiting challenge compounds the timeline problem. Retail media strategies often target specific customer segments—frequent online grocery shoppers, cross-channel buyers, category-specific purchasers. Traditional research panels struggle to efficiently identify and recruit these audiences. An agency seeking to understand how "customers who buy premium pet food both online and in-store" navigate retail media environments might spend three weeks just assembling an appropriate participant pool.

Moderator availability creates another bottleneck. Skilled qualitative researchers who understand both retail media dynamics and effective interviewing techniques represent scarce resources. Scheduling their availability, coordinating with participant schedules, and accommodating timezone differences extends timelines further. For agencies managing multiple client requests simultaneously, moderator capacity becomes a binding constraint on research throughput.

The analysis phase presents its own delays. Converting interview recordings into transcripts, coding responses, identifying patterns, and synthesizing findings into actionable recommendations requires substantial time investment. When agencies need insights to inform immediate decisions—which channels deserve testing, what creative approaches merit development, how to structure measurement frameworks—the traditional analysis timeline misaligns with strategic urgency.

How Conversational AI Transforms the Research Timeline

Voice AI platforms compress the research timeline from weeks to days by automating recruitment, conducting interviews, and generating insights simultaneously across dozens or hundreds of participants. The agency facing the retail media prioritization question deployed conversational AI to interview 85 customers who had purchased their client's product category within the past 90 days. The entire process—from research design to actionable insights—completed in 72 hours.

The AI interviewer engaged each participant in natural conversation, adapting questions based on responses while maintaining methodological consistency across all interviews. When a participant mentioned discovering products through Amazon's "frequently bought together" recommendations, the AI probed deeper: "Walk me through what you were originally shopping for and how you ended up considering this product." When another described using Instacart primarily for stock-up trips, the AI explored: "How does your approach to discovering new products differ between Instacart and your in-store shopping?"

This adaptive interviewing capability mirrors what skilled human moderators do—following interesting threads while ensuring core research questions get addressed—but scales across dozens of simultaneous conversations. Each interview maintains the depth and nuance of one-on-one dialogue without requiring moderator scheduling or participant coordination. Participants complete interviews at their convenience, typically spending 15-20 minutes in conversation that feels natural rather than transactional.

The platform's multimodal capabilities proved particularly valuable for retail media research. Participants shared screens to show their actual shopping behavior—demonstrating how they navigated Amazon's search results, responded to Walmart Connect's homepage placements, or used Instacart's category browsing. These behavioral demonstrations revealed gaps between stated preferences and actual practices. Several participants claimed they "never clicked on ads" but screen recordings showed them regularly engaging with sponsored products, simply not recognizing them as advertising.

Analysis happened continuously as interviews progressed. The AI identified emerging patterns—how customers distinguished between "shopping for specific items" versus "open to discovery," which retail environments aligned with each mindset, what triggered consideration of unfamiliar brands. By the time all 85 interviews completed, the platform had generated preliminary insights the agency could immediately explore and validate.

What Agencies Actually Learn About Channel Behavior

The retail media research revealed nuanced customer behaviors that performance data alone couldn't illuminate. Amazon emerged as the dominant channel for "mission-based" shopping—customers arrived knowing what they wanted and used search to find specific products. Sponsored product ads performed well in this context because they appeared within the consideration set customers actively evaluated. However, brand switching happened primarily on price and availability rather than discovery or persuasion.

Walmart Connect showed different behavioral patterns. Customers described browsing Walmart.com more exploratorily, often starting with broad category searches or homepage browsing before narrowing to specific products. This created more opportunity for discovery-oriented advertising. Sponsored placements that highlighted product benefits or use cases generated consideration among customers who hadn't arrived with predetermined brand preferences. The agency recognized this insight explained Walmart Connect's stronger ROAS—the channel captured customers earlier in the decision process when they remained open to influence.

Instacart revealed an unexpected pattern. Customers approached the platform with time pressure and basket-building mindsets—they needed to assemble a complete shop within a constrained delivery window. This urgency made them more likely to stick with familiar brands unless a compelling reason to switch appeared immediately visible. Sponsored products succeeded when they solved specific problems ("organic option," "larger size," "better value") rather than when they simply promoted brand awareness. The agency realized Instacart required different creative strategy emphasizing functional benefits over brand building.

Target Roundel operated in a hybrid space. Customers shopping Target.com often combined mission-based purchases (specific items they needed) with exploratory browsing (seeing what's new or on sale). This dual mindset created opportunities for both consideration-stage advertising (reaching customers early in category exploration) and conversion-focused placements (capturing customers ready to buy). The agency identified Target as particularly valuable for product launches where the goal combined awareness building with immediate sales.

Cross-channel behavior emerged as a critical insight. Customers rarely exhibited pure loyalty to single retail environments. Instead, they distributed purchases based on contextual factors—urgency, basket composition, delivery needs, price sensitivity in the moment. A customer might buy cleaning supplies through Instacart during a weekly stock-up, purchase the same brand on Amazon when they ran out unexpectedly, and add it to their cart at Walmart.com while browsing for other items. This behavior suggested retail media strategies needed to consider customer journey stages rather than treating channels as independent silos.

The research also revealed how customers perceived and responded to different ad formats. Sponsored search results generated clicks primarily when they matched specific intent—customers searching for "organic coffee" engaged with sponsored organic coffee brands. Homepage placements and category page ads captured attention during exploratory browsing but required immediate relevance to generate consideration. Video ads and rich media formats rarely influenced purchase decisions in the moment but occasionally created awareness that surfaced later in the journey.

Perhaps most valuable, the research identified language and framing that resonated within different retail contexts. Customers described Amazon purchases using efficiency-oriented language ("quick reorder," "Prime delivery," "same as last time"). Walmart shopping incorporated more value-conscious framing ("good deal," "comparable quality," "worth the price"). Instacart conversations centered on convenience and time-saving ("delivered in an hour," "didn't have to go to the store," "saved me a trip"). These linguistic patterns suggested creative messaging should adapt to match the mindset customers brought to each environment.

Translating Insights Into Channel Strategy

The agency synthesized these behavioral insights into a prioritized retail media strategy that aligned budget allocation with customer behavior rather than simply scaling historical performance. Amazon received continued investment but with refined expectations—the channel would drive volume among existing customers and capture high-intent searches, but shouldn't be expected to generate significant new customer acquisition or brand building. Creative strategy emphasized availability, competitive pricing, and Prime eligibility rather than brand storytelling.

Walmart Connect earned increased investment based on its discovery-oriented customer behavior. The agency recommended testing homepage placements and category page sponsorships with creative that highlighted product benefits and use cases. Rather than optimizing purely for immediate ROAS, the measurement framework incorporated consideration metrics and new customer acquisition to capture Walmart's role in expanding the brand's customer base.

Instacart strategy shifted toward problem-solution messaging that addressed the time-pressured, basket-building mindset customers brought to the platform. The agency recommended sponsored product placements that emphasized functional benefits ("organic," "family size," "better value") rather than brand-building creative. Budget allocation focused on high-frequency purchase categories where the brand offered clear advantages over competitors.

Target Roundel emerged as the priority channel for product launches and seasonal campaigns where the goal combined awareness with conversion. The agency designed a testing approach that layered homepage placements (for awareness) with category page sponsorships (for consideration) and sponsored product ads (for conversion), measuring how these formats worked together to drive both immediate sales and longer-term brand building.

The cross-channel insights informed a measurement framework that tracked customer journeys across retail environments rather than attributing value to isolated touchpoints. The agency implemented a model that recognized Amazon's role in converting customers who discovered the brand through Walmart, acknowledged Instacart's function in driving repeat purchases after Target introductions, and measured how different channels contributed to overall brand growth rather than competing for last-click attribution.

Budget allocation reflected these strategic priorities while maintaining flexibility for optimization. The agency proposed a 70-20-10 split: 70% to proven channels (Amazon, Walmart) with clear performance expectations, 20% to expansion opportunities (Target, Instacart) with learning-oriented measurement, and 10% to testing emerging platforms (Kroger Precision Marketing, Albertsons Media Collective) to build knowledge for future scaling. This structure balanced performance accountability with strategic exploration.

The Methodological Advantage for Agencies

Beyond speed, conversational AI provides agencies with methodological capabilities that strengthen the quality and credibility of insights. The platform's consistent interviewing approach eliminates moderator variability—every participant receives the same core questions while the AI adapts follow-up probes based on responses. This consistency allows agencies to compare insights across customer segments, product categories, and time periods without worrying that differences reflect interviewing style rather than actual behavioral variation.

The scale of research changes what becomes knowable. Traditional qualitative research typically involves 8-12 interviews per study, enough to identify major themes but insufficient to understand variation within customer segments. Conversational AI enables 50, 100, or 200 interviews per study, revealing nuanced patterns that smaller samples miss. The retail media research identified distinct behavioral profiles within the "frequent online grocery shopper" segment—some prioritized speed and convenience, others focused on value and selection, still others sought specific product attributes. These profiles emerged from analyzing patterns across 85 interviews, a sample size traditional qualitative research rarely achieves.

The platform's analytical capabilities help agencies move from observation to explanation. Rather than simply reporting what customers said, the AI identifies underlying behavioral patterns, connects related themes, and surfaces unexpected insights. The retail media analysis revealed that customers who described themselves as "loyal Amazon shoppers" actually distributed purchases across multiple platforms based on contextual factors—a finding that challenged conventional assumptions about channel loyalty and suggested more sophisticated attribution models.

Longitudinal research becomes practical at scale. Agencies can re-interview the same customers over time to understand how behaviors evolve, measure the impact of campaigns, and track changes in brand perception. One agency now conducts quarterly check-ins with a consistent panel of customers, building a longitudinal dataset that reveals seasonal patterns, competitive dynamics, and the cumulative effect of retail media investments. This ongoing research costs a fraction of traditional tracking studies while delivering richer qualitative context.

The 98% participant satisfaction rate that User Intuition consistently achieves reflects the quality of the conversational experience. Customers describe interviews as "natural," "easy," and "interesting"—quite different from typical research participation. This satisfaction translates into higher completion rates, more thoughtful responses, and greater willingness to participate in follow-up research. Agencies building longitudinal panels benefit from this positive experience, as customers remain engaged over multiple research waves.

Practical Implementation for Agency Teams

Agencies integrating conversational AI into their research practice need to rethink how they structure engagements and set client expectations. The traditional model—propose research, wait weeks for insights, present findings—gives way to a more iterative approach where initial insights arrive quickly and subsequent research refines understanding based on what emerges.

Research design becomes more important when execution happens quickly. Agencies should invest time upfront clarifying research questions, identifying target segments, and designing interview guides that balance structure with flexibility. The retail media study succeeded because the agency clearly defined what they needed to understand: how customers discovered products, what triggered consideration, how different retail environments shaped behavior, and what messaging resonated within each context. This clarity allowed the AI to conduct focused interviews that generated actionable insights rather than interesting but unfocused observations.

Client education matters. Many clients remain unfamiliar with AI-powered research and may question its validity compared to traditional methodologies. Agencies should proactively address these concerns by explaining the methodology, sharing sample interviews, and highlighting the platform's 98% participant satisfaction rate. Positioning conversational AI as "qualitative research at quantitative scale" helps clients understand they're getting depth and nuance, just faster and at larger sample sizes than traditional approaches allow.

Integration with existing research practices strengthens overall insights quality. Conversational AI excels at understanding customer behavior, motivations, and decision processes—the "why" behind actions. Quantitative data reveals patterns and measures magnitude—the "what" and "how much." Combining these approaches creates more complete understanding. The retail media agency used conversational AI to understand behavioral drivers, then validated findings through analysis of actual purchase data and campaign performance metrics.

Agencies should consider building research into ongoing client relationships rather than treating it as episodic projects. Monthly or quarterly research check-ins create continuous learning loops that inform strategy, measure impact, and identify emerging opportunities. This ongoing research costs less than traditional approaches while generating more timely insights that can actually influence decisions before they're made.

What This Means for Retail Media Strategy

The shift toward customer-informed retail media strategy represents more than methodological improvement—it changes how agencies compete and deliver value. When every agency has access to the same performance data and channel sales pitches, differentiation comes from understanding customer behavior more deeply than competitors. Conversational AI makes this depth practical and scalable.

Agencies can now offer clients something beyond media buying expertise: behavioral intelligence that informs not just where to invest but how to show up within each channel. This intelligence extends beyond initial strategy into creative development, measurement framework design, and ongoing optimization. The agency that understands how customers think about categories, navigate retail environments, and respond to different messages delivers more value than the agency that simply manages campaigns efficiently.

The speed of insights changes client relationships. Rather than presenting research findings weeks after questions arise, agencies can incorporate customer voice into active strategy discussions. When a client asks "should we test Kroger Precision Marketing," the agency can interview 50 relevant customers and return with behavioral insights within a week. This responsiveness builds trust and positions the agency as a strategic partner rather than a vendor executing predetermined plans.

For the retail media ecosystem overall, customer-informed strategy should improve outcomes for brands, retailers, and customers. Brands invest in channels where their customers actually shop and respond to messages that resonate with real decision processes. Retailers see better campaign performance as brands align creative strategy with customer behavior within their environments. Customers encounter more relevant advertising that helps rather than interrupts their shopping experience.

The methodology also reveals opportunities traditional research misses. The retail media study identified micro-moments when customers became receptive to new brands—specific contexts where openness to switching temporarily increased despite general category loyalty. These moments (running out of a product unexpectedly, shopping for a special occasion, responding to a specific need) represent high-value targeting opportunities that performance data alone wouldn't illuminate.

Looking Forward

As retail media continues expanding—with more retailers launching networks and brands distributing larger budgets across platforms—the strategic challenge intensifies. Success requires understanding not just channel performance but customer behavior within channels. Agencies that build this understanding systematically, using tools that deliver qualitative depth at quantitative speed, will differentiate their work and deliver measurably better outcomes.

The research methodology itself will continue evolving. Current conversational AI capabilities focus on understanding existing behavior and preferences. Future developments will likely enable more sophisticated scenario testing ("how would you respond if..."), longitudinal tracking that measures behavior change over time, and integration with behavioral data that connects stated preferences to actual actions.

The broader implication extends beyond retail media to any domain where agencies need to understand customer behavior quickly enough to inform active decisions. Product development, brand positioning, customer experience optimization, content strategy—all benefit from qualitative insights delivered at quantitative speed. Agencies building competency in AI-powered research position themselves to deliver evidence-based strategy across their entire service offering.

The retail media agency that started this story delivered their channel prioritization recommendation on schedule, backed by insights from 85 customer interviews conducted in 72 hours. The client approved the strategy, implemented the recommended approach, and saw 28% improvement in blended ROAS over the subsequent quarter. More importantly, they understood why the strategy worked—not because the agency picked winning channels, but because they aligned investments with how customers actually shopped.

That understanding—grounded in customer voice rather than channel sales pitches or historical performance alone—represents the future of retail media strategy. Conversational AI makes it practical, scalable, and fast enough to inform decisions that matter. Agencies ready to integrate these capabilities into their practice will find themselves better equipped to navigate the complexity of modern retail media and deliver outcomes that justify client trust.